Existing LDP protocols for frequency estimation typically focus on one-time (i.e., static) computation [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}.
However, considering both evolving data and the continuous monitoring together, pose a significant challenge under LDP guarantees.
For instance, the naïve solution in which a locally differentially private computation is repeated, will quickly increase the privacy loss leading to large values of \(\epsilon \)  due to the sequential composition theorem in DP [9]} while being prone to “averaging attacks” [10]}.
To tackle this issue, most state-of-the-art solutions relies on memoization [10]}, [12]}, [13]}, [14]}, [15]}.
